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Using KM to Make Problem-Solving Reusable

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Taliano mentioned that this technology can generate some of these tags for users, which they can either approve or deny. Regardless, folksonomies and taxonomies are integral for describing enterprise content, and supplying context for it, for use in downstream applications. “With generative AI, the content becomes as important as the context,” Smith suggested. “Ultimately, what you’re looking at for organizations to create is good content with great context, and that’s the information architecture piece. Before you expose things to generative AI and ground what generative AI can do, you’ve got to think about context and what content you’re exposing.”

Information Architecture and Content Presentation

The dichotomy between context and content that Smith described is fundamental to the tenet of information archi- tecture, which is centered on ordering and presenting content in a way that makes sense to end users. For KM, this notion relates to “not just metadata, but the actual content itself,” Smith noted. “How you create [correct] knowledge content, whether that’s a template to guide or check a how-to article, a process diagram, or whatever it is that drives professionals to do better or more consistent work. ...” Credible KM solutions have a wealth of such constructs to make knowledge reusable for solving problems, including a formal marketplace for accessing solutions for specific verticals, use cases, and applications.

According to Laserfiche CTO Michael Allen, within this framework, customers can access “templates and workflows that are prepackaged. And, we have a marketplace that allows those to be shared, downloaded, and monetized.” The specific workflows and templates Allen referred to are facilitated through partnerships Laserfiche has with specialists across verticals. For example, for the contract management process, in which several parties are responsible for collaborating to review and update contracts, “for certain verticals, like higher education, there’s a flavor to it,” Allen observed. “The way they do it, they have a certain workflow where certain offices are involved in the review of certain categories of contracts. We have solutions for things like that.”

Workflows

The workflow concept described by Allen has always been integral to KM’s ability to render problem solving reusable. It’s aligned with the notion of information architecture in that it involves a specific means for knowledge practitioners to collaborate on content. Moreover, workflows are applicable across organizations. “Administrative or back-office workflows [are not] really differentiated between different institutions,” Allen admitted. “Most hospitals, or most manufacturing organizations, or most higher-ed organizations ... have a common set of back-office processes that are very, very similar across the different institutions.” In some situations, coupling the workflow concept with digital agents powered by generative models—which many today refer to as ‘agentic AI,’ can yield profound results for increasing efficiency.

Deedee Kato, VP of corporate marketing at Foxit, envisioned a reality in which users prompt such an agent via “smart commands” to have AI assistants complete tasks of significant magnitude. “In the future, we’re looking to make multi-step smart commands,” Kato revealed. “So, you can write a prompt that says, ‘Make this PDF court-ready,’ then it would do 10 steps.” Such multi-dimensionality, or the multi-step nature of workflows, makes them particularly tenable for KM use cases because almost anything can be incorporated into them. “It’s really common, if you’re in higher education, if it involves the admissions office, and they have a contract for a software or some vendor, that the head of that office is going to have to sign off on it if it’s above a certain value,” Allen said. “That can be encoded into a workflow.”

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